Memory-based vector quantization of LSF parameters by a power series approximation
Journal article, 2007
Abstract: In this paper, memory-based quantization is studied in detail. We propose a new framework, Power
Series Quantization (PSQ), for memory-based quantization. With LSF quantization as the application, several
common memory-based quantization methods (FSVQ, predictive VQ, VPQ, safety-net etc.) are analyzed and
compared with the proposed method, and it is shown that the proposed method performs better than all other
tested methods. The proposed PSQ method is fully general, in that it can simulate all other memory-based
quantizers if it is allowed unlimited complexity.